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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Leveraging Convolutional Neural Networks for Multiclass Waste Classification Angdresey, Apriandy; Kairupan, Indah Yessi; Mongkareng, Andre Gabriel
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9373

Abstract

The impact of population growth on waste production in Indonesia emphasizes the urgent need for effective waste management to mitigate environmental and health risks. Segregating waste into organic and inorganic categories is essential for sustainable management, enabling processes like composting and recycling. Employing convolutional neural networks (CNN) through machine learning presents a promising solution for waste classification. This study utilizes a CNN algorithm to achieve significant accuracy and precision in multi-class waste classification, with particular attention to areas for improvement, such as cardboard classification. Based on the MobileNetV2 architecture and Adam optimizer, the model demonstrates high accuracy and precision, with training and validation accuracy of 95.28% and 89.48%, respectively. High precision and recall values confirm its accurate waste classification. The evaluation of unseen data maintains an accuracy of 86.36%, indicating its generalization ability. However, variations in accuracy among waste classes suggest opportunities for refinement, particularly in cardboard classification.
Comparative Study of Support Vector Regression and Long Short-Term Memory for Stock Price Prediction Aviva Pradasyah; Baita, Anna
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9425

Abstract

This study aims to compare the performance of two machine learning algorithms, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), in predicting the stock prices of PT Bank Rakyat Indonesia (BBRI) using daily historical data from January 1, 2020, to January 10, 2025. The data were processed using a 60-day sliding window technique and normalized with MinMaxScaler. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the coefficient of determination (R²) across five independent trials (5-fold trials). The evaluation results show that SVR outperforms in short-term prediction, with an average MAE of 0.0281, MSE of 0.0014, and R² of 0.9072. Meanwhile, LSTM records an average MAE of 0.0312, MSE of 0.0015, and R² of 0.8962, but achieves better performance in medium-term predictions, with a smaller average error of Rp228.02 compared to Rp242.52 from SVR. Both models demonstrate strong generalization capabilities on test data without signs of overfitting. Based on these findings, SVR is recommended for stable short-term forecasts, while LSTM is better suited for medium-term predictions involving complex trend patterns.
UAV Image Classification of Oil Palm Plants Using CNN Ensemble Model Lestandy, Merinda; Nugraha, Adhi
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9437

Abstract

Basal Stem Rot (BSR), caused by Ganoderma boninense, is one of the most destructive diseases affecting oil palm plantations in Southeast Asia. Early detection of this disease is crucial to prevent its widespread transmission and to maintain plantation productivity. This study proposes an image classification approach using ensemble learning with three Convolutional Neural Network (CNN) architectures: DenseNet161, ResNet152, and VGG19, to detect BSR-infected oil palm trees through aerial imagery captured by Unmanned Aerial Vehicles (UAVs). The dataset used consists of 7,348 annotated images classified into two categories: healthy and unhealthy. Experimental results show that the DenseNet161 model outperformed the others, achieving a validation accuracy of 91.75% and a validation loss of 0.0307. The ensemble CNN approach demonstrated improved classification accuracy and holds significant potential for implementation in automated and precise plant health monitoring systems. This research provides a valuable contribution to AI-based agricultural technology, particularly in disease management for oil palm plantations.
Prototype of Implementation of Smart Contract for Blockchain-Based Document Storage Pangidoan, Annes Maria; Buana, Putu Wira; Purnama, Fajar
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9493

Abstract

Data, including digital and physical documents, is a valuable asset often vulnerable to forgery, theft, and reliance on centralized servers, which are costly and prone to failure. This study develops a prototype of a decentralized document storage application by combining blockchain and the InterPlanetary File System (IPFS). The system is designed as a web-based decentralized application (DApp), integrating Ethereum smart contracts to immutably record document metadata and access history, while the actual files are stored in IPFS and identified using unique Content Identifiers (CIDs). User interactions are facilitated through MetaMask for authentication and transaction approval. The system is developed using the Waterfall methodology. Functional testing is conducted through unit tests using Ganache as a local Ethereum blockchain, and the smart contract is also deployed to the Sepolia Ethereum testnet. The results show that the system successfully stores documents via IPFS and records metadata and access activities transparently on the blockchain. Access and download tracking features enhance document accountability. This solution provides a secure, efficient, and transparent alternative to centralized document storage and contributes to the advancement of distributed digital archiving systems.
Static Analysis-Based Security Enhancement for Mobile Applications Using Mobile Security Framework (MOBSF) Nur Izzati, Putri; Kasmawi, Kasmawi
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9525

Abstract

Mobile application security is crucial to protect users’ personal data and maintain trust in the application. Without proper security testing, an app becomes vulnerable to threats such as data theft and cyber attacks. This study aims to identify and fix security vulnerabilities in the XYZ mobile application, a social platform used to report domestic violence and child sexual abuse cases. The analysis was conducted using static analysis with the Mobile Security Framework (MOBSF). The XYZ app was developed using Flutter and falls under the hybrid application category. Since it handles sensitive information from victims and reporters, ensuring its security is essential. The analysis revealed four major vulnerabilities with high risk levels, mainly related to misconfiguration and weak security settings. After addressing these issues, the app’s security score improved from 37/100 (high risk) to 61/100 (low risk). These improvements were implemented in the final development phase before the app was released to users. MOBSF helped developers detect potential vulnerabilities early through static analysis, serving as a security baseline. This approach ensured the app no longer contained risks such as debug certificates, enabled debug mode, or support for outdated Android versions. The findings show that MOBSF-based security analysis is effective in detecting and reducing application security weaknesses, making the XYZ app more secure in protecting user data.
IoT-Based UPS Device Electricity Usage Monitoring System with MQTT Protocol Adit Oktopryadin; Adi Purnama
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9530

Abstract

The continuity of network device operations heavily relies on stable power supply, especially in digital environments that demand uninterrupted connectivity. One commonly used solution to ensure power continuity is the Uninterruptible Power Supply (UPS). However, traditional UPS systems often lack real-time monitoring mechanisms, leaving users uninformed during the transition from main electricity to UPS power. To address this challenge, this study proposes the design of a UPS power consumption monitoring system based on the Internet of Things (IoT) using the Message Queuing Telemetry Transport (MQTT) communication protocol. The system integrates a PZEM-004T power sensor and ESP32 microcontroller to read electrical parameters such as voltage, current, and power in real-time, and displays the data through a digital dashboard built with Node-RED. The implementation results show that the system can automatically detect changes in power source status and record electrical parameters with an average error rate below 1%, both during normal grid operation and when switching to UPS power. This system is expected to serve as a practical and efficient solution for minimizing network downtime caused by power disruptions.
Layered Image Encryption Method Based on Combination of Logistic Map, Henon Map, and Sine Map to Enhance Digital Image Security Amir Musthofa; Moses Setiadi, De Rosal Ignatius
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9569

Abstract

In today's digital era, ensuring the confidentiality of image data is crucial due to the widespread use of images in fields such as medical imaging, military communication, and multimedia applications. This study proposes a layered image encryption method by integrating three chaotic systems: Logistic Map, Henon Map, and Sine Map. Each layer in the encryption process applies a different chaotic map to sequentially perform pixel permutation, XOR-based substitution, and modulus-based substitution. Key generation is carried out by producing pseudo-random number sequences derived from the iterations of each chaotic map: the Logistic Map (using specific initial and control parameters), the Henon Map (with two initial condition variables), and the Sine Map (based on a sine function), all of which are highly sensitive to initial conditions and control parameters. These sequences are then used as keys in each encryption stage. The proposed method strengthens the principles of confusion and diffusion, thereby enhancing the security and randomness of the encrypted images. Evaluation was conducted using metrics such as histogram analysis, entropy, chi-square, correlation coefficient, PSNR, and BER. The experimental results demonstrate that the method produces encrypted images with strong statistical characteristics and high resilience against common cryptographic attacks. Thus, this approach makes a significant contribution to the development of secure and efficient image encryption techniques based on chaos theory.
Human Vulnerabilities to Social Engineering Attacks: A Systematic Literature Review for Building a Human Firewall Tsauri, Muhammad Shofian
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9585

Abstract

Social engineering attacks exploit human psychology to deceive individuals into compromising information security, making the human element a critical vulnerability in cybersecurity systems. This study aims to identify and analyze patterns of human susceptibility in social engineering through a systematic literature review (SLR). Guided by the PRISMA 2020 protocol, a total of 865 articles were initially retrieved from databases such as Scopus, IEEE Xplore, ResearchGate, and Google Scholar. After applying strict inclusion and exclusion criteria, 39 peer-reviewed articles published between 2020 and 2024 were selected for thematic synthesis. The results reveal recurring human vulnerability factors including low security awareness, emotional manipulation (e.g., fear, urgency), overtrust in authority, and lack of behavioral control. These vulnerabilities manifest in predictable victim profiles and behavioral patterns, which are often exploited through phishing, pretexting, and other deception-based tactics. Furthermore, the review highlights the limitations of current mitigation strategies that focus solely on technical solutions without integrating human behavior models. The findings serve as a conceptual foundation for building a “human firewall,” emphasizing awareness, vigilance, and behavioral training as integral components of social engineering defense. This study also lays the groundwork for the development of a human-centric detection model in future research, particularly in the context of mobile banking.
Implementation of Blockchain Smart Contract for Online Concert Ticket Transactions Based on NFTs Lingga Pratyaksa Nugraha, Anak Agung; Emmy Rosiana Dewi , Ni Wayan; Purnama, Fajar
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9598

Abstract

The development of the entertainment industry, especially music concerts, has driven the transformation of ticket sales systems from conventional to digital methods. Although online concert ticket sales offer greater convenience and reach, they still face the risks of fraud, counterfeit tickets, and unfair distribution. This study, Blockchain Smart Contract Implementation for NFT-Based Online Music Concert Ticket Transactions, aims to develop a ticket sales system using blockchain technology by integrating smart contracts and Non-Fungible Tokens (NFTs). The main objectives are to design and implement smart contracts on the Ethereum network, implement ERC-721-based digital tickets, ensure transparency in transaction history, and verify ticket authenticity through unique identifiers. This study adopts the Agile method, with implementation on the Ethereum Sepolia Testnet and testing using the meta mask digital wallet. The results show that the developed system can automatically hold funds through an escrow mechanism until the ticket is downloaded, generate unique and tamper-proof NFT tickets, display transaction details transparently, and facilitate ticket verification effectively. In conclusion, the use of smart contracts and NFTs significantly improves the security, transparency, and trustworthiness of online music concert ticket transactions.
Topic Modeling of Skincare Comments from Female Daily Nabila, Nabila; Ratnasari, Chanifah Indah
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9625

Abstract

The increasing popularity of skincare products in Indonesia has encouraged many consumers to seek and share information through online platforms. One of the most influential platforms is Female Daily, which provides a space for users to review and discuss various skincare products. This study aims to explore the dominant topics within user-generated comments related to skincare products on Female Daily. The research employed a descriptive qualitative approach using topic modeling with Latent Dirichlet Allocation (LDA). Data were collected from user comments on several popular skincare products and were preprocessed through punctuation removal, case folding, tokenization, normalization, stopword removal, and stemming. The optimal number of topics was determined using coherence scores. The results reveal that users frequently discuss personal experiences, highlight product benefits and drawbacks, and often refer to their specific skin concerns. These insights provide valuable information for skincare brands to understand customer preferences and perceptions. In conclusion, topic modeling with LDA proves effective in extracting meaningful themes from large-scale textual data, offering a useful method for analyzing consumer feedback in the beauty industry.

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